2006 PreemptiveIEUsingUnresRelDiscov
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- (Shinyama & Sekine, 2006) ⇒ Yusuke Shinyama, Satoshi Sekine. (2006). “Preemptive Information Extraction Using Unrestricted Relation Discovery.” In: Proceedings of the HLT-NAACL Conference (HLT-NAACL 2006).
Subject Headings: Unrestricted Relation Discovery Task, Open Information Extraction.
Notes
Cited By
2007
- (Banko et al., 2007) ⇒ Michele Banko, Michael J. Cafarella, Stephen Soderland, Matt Broadhead, and Oren Etzioni. (2007). “Open Information Extraction from the Web.” In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2007).
- Also this year, (Shinyama & Sekine, 2006) described an approach to “unrestricted relation discovery” that was developed independently of our work, and tested on a collection of 28,000 newswire articles. This work contains the important idea of avoiding relation-specificity, but does not scale to the Web as explained below. Given a collection of documents, their system first performs clustering of the entire set of articles, partitioning the corpus into sets of articles believed to discuss similar topics. Within each cluster, named-entity recognition, co-reference resolution and deep linguistic parse structures are computed and then used to automatically identify relations between sets of entities. This use of “heavy” linguistic machinery would be problematic if applied to the Web. Shinyama and Sekine’s system, which uses pairwise vector-space clustering, initially requires an O(D2) effort where D is the number of documents. Each document assigned to a cluster is then subject to linguistic processing, potentially resulting in another pass through the set of input documents. This is far more expensive for large document collections than TEXTRUNNER’s O(D+T log T ) runtime as presented earlier. From a collection of 28,000 newswire articles, Shinyama and Sekine were able to discover 101 relations. While it is difficult to measure the exact number of relations found by TEXTRUNNER on its 9,000,000 Web page corpus, it is at least two or three orders of magnitude greater than 101.
Quotes
Abstract
- We are trying to extend the boundary of Information Extraction (IE) systems. Existing IE systems require a lot of time and human effort to tune for a new scenario. Preemptive Information Extraction is an attempt to automatically create all feasible IE systems in advance without human intervention. We propose a technique called Unrestricted Relation Discovery that discovers all possible relations from texts and presents them as tables. We present a preliminary system that obtains reasonably good results.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2006 PreemptiveIEUsingUnresRelDiscov | Satoshi Sekine Yusuke Shinyama | Preemptive Information Extraction Using Unrestricted Relation Discovery | http://cs.nyu.edu/yusuke/research/hlt-naacl-2006-preemptive-information-extraction-using-unrestricted-relation-discovery-paper.pdf |